- Underwater image restoration
Underwater images are easily degraded by various characteristics of underwater environment. We propose an underwater image enhancement algorithm which improves the low contrast and alleviates the color distortion. We first estimate the gradient of original scene image by scaling the gradient of an input underwater image with a corresponding transmission map. Then we reconstruct an original scene image from the compensated gradient image using a constraint. Experimental results demonstrate that the proposed algorithm improves the contrast and alleviates the color distortion faithfully, and outperforms the state-of-the-art underwater image enhancement method.
- Image and video dehazing
A fast and optimized dehazing algorithm for hazy images and videos is proposed in this work. Based on the observation that a hazy image exhibits low contrast in general, we restore the hazy image by enhancing its contrast. However, the overcompensation of the degraded contrast may truncate pixel values and cause information loss. Therefore, we formulate a cost function that consists of the contrast term and the information loss term. By minimizing the cost function, the proposed algorithm enhances the contrast and preserves the information optimally. Moreover, we extend the static image dehazing algorithm to real-time video dehazing. We reduce flickering artifacts in a dehazed video sequence by making transmission values temporally coherent. Experimental results show that the proposed algorithm effectively removes haze and is sufficiently fast for real-time dehazing applications.